Both water and hydrocarbons in earth formations produce detectable NMR signals. It is desirable that the signals from water and hydrocarbons be separable so that hydrocarbon-bearing zones may be identified. However, it is not always easy to distinguish which signals are from water and which are from hydrocarbons. Various methods have been proposed to separately identify water and hydrocarbon signals.
The differential spectrum (DSM) and shifted spectrum (SSM) methods proposed by Akkurt et. al. in “NMR Logging of Natural Gas Reservoirs” Paper N. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1995, compare T2 distributions derived from two Carr-Purcell-Meiboom-Gill (CPMG) measurements performed with different polarization times (DSM) or echo-spacings (SSM). A modification to these methods, known as time domain analysis (TDA), was later introduced by Prammer et al. in “Lithology-Independent Gas Detection by Gradient-NMR Logging,” SPE paper 30562, 1995. In TDA, “difference” data are computed directly in the time domain by subtracting one set of the measured amplitudes from the other. The difference dataset is then assumed to contain only light oil and/or gas. In TDA, relative contributions from light oil or gas are derived by performing a linear least squares analysis of the difference data using assumed NMR responses for these fluids. Both DSM and TDA assume that the water signal has substantially shorter T1 relaxation times than those of the hydrocarbons. This assumption is not always valid, however. Most notably, this assumption fails in formations where there are large pores or where the hydrocarbon is of intermediate or high viscosity. The SSM method and its successor, the enhanced diffusion method (EDM) proposed by Akkurt et. al. in “Enhanced Diffusion: Expanding the Range of NMR Direct Hydrocarbon Typing Applications”, Paper GG. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1998, separate gas, oil and water contributions based on changes in the T2 distributions that result from changes in the echo spacing of CPMG measurements. The methods are applicable in a limited range of circumstances and the accuracy of the result is significantly compromised by incomplete separation of water and hydrocarbon signals in the T2 domain. Moreover, these methods are designed to function with CPMG sequences. However, with the diffusion-based methods, CPMG pulse sequences provide poor signal to noise ratios due to the reduced number of echoes that can be measured. A strategy for combining and selecting these different NMR methods has been described recently by Coates et al. in U.S. Pat. No. 6,366,087 B1.
The diffusion-editing (DE) pulse sequence by Hürlimann et al. provides a different approach. See M. D. Hürlimann et al., “Diffusion-Editing: New NMR Measurement of Saturation and Pore Geometry,” paper presented at the 2002 Annual Meeting of the Society of Professional Well Log Analysts, Osio, Japan, Jun. 2–5; see also, U.S. Pat. No. 6,570,382, filed on Nov. 28, 2000, by Hürlimann. This patent is assigned to the same assignee as the present invention and is hereby incorporated by reference. DE pulse sequences are similar to the CPMG sequences except that the initial two echoes are acquired with longer echo spacings and the third and subsequent echoes are acquired with shorter echo spacings. In DE pulse sequences, diffusion information is encoded during the acquisition of the first two echoes, whereas the third and subsequent echoes provide bulk and surface relaxation time information with relatively little attenuation of the signal by diffusion. Using a conventional CPMG sequence to encode the diffusion information requires a long inter-echo spacing, which results in poor bulk and surface relaxation time information because diffusion decay attenuates the signal after relatively few echoes. Consequently, a suite of data acquired with DE sequences provides better diffusion information and signal-to-noise ratio in the spin-echo data, as compared to an analogous suite acquired with CPMG sequences. Therefore, DE sequences can provide more accurate and robust computations of brine and oil T2 distributions than CPMG sequences.
In addition to DE sequences, specialized interpretation methods have been developed for NMR data in order to further enhance hydrocarbon detection. These methods typically apply forward modeling to suites of NMR data acquired with different parameters. The suite of NMR data are typically acquired with different echo spacings (TE) or polarization times (WT), and sometimes acquired with different magnetic field gradients (G). DE sequences are one example of such data acquisition. Two exemplary methods include: the MACNMR proposed by Slijkerman et al., SPE paper 56768, “Processing of Multi-Acquisition NMR Data”, 1999, and the Magnetic Resonance Fluid characterization (MRF) methoddisclosed in U.S. Pat. No. 6,229,308 B1 issued to Freedman and assigned to the assignee of the present invention (“the Freedman patent”). The Freedman patent is hereby incorporated by reference.
The MRF method is capable of obtaining separate oil and water T2 distributions. This method uses a Constituent Viscosity Model (CVM), which relates relaxation time and diffusion rates to constituent viscosities whose geometric mean is identical to the macroscopic fluid viscosity. With the MRF method, estimates for water and hydrocarbon volumes are obtained by applying a forward model to simulate the NMR responses to a suite of NMR measurements acquired with different parameters. Specifically, The MRF technique is based on established physical laws which are calibrated empirically to account for the downhole fluid NMR responses. By using realistic fluid models, MRF aims to minimize the number of adjustable parameters to be compatible with the information content of typical NMR log data. Since the model parameters are by design related to the individual fluid volumes and properties, determination of the parameter values (i.e. data-fitting) leads directly to estimates for petrophysical quantities of interest.
The forward-model approach relies on the validity of the fluid models employed. In “non-ideal” situations where fluid NMR responses deviate from the model behavior (oil-wet rocks, restricted diffusion), these techniques may lead to erroneous answers. In some circumstances, “nonideal”responses may be identified by poor fit-quality, in which case the fluid models can be adjusted by modifying the appropriate model parameter. However, it may not be obvious which element of the fluid model should be modified and what modification is needed.
Another approach developed by Schlumberger, based on a maximum entropy principle (MEP), consists of a general model-independent method to analyze complex fluids data acquired with NMR logging instruments and present the results in a visually attractive and easy-to-understand format, hereby referred to as Diffusion-Relaxation maps, or D-T2 maps. These maps have been used to understand cases where model-based analysis gives unsatisfactory results because of deviations of NMR properties from the “ideal” behavior assumed in the models. These situations can arise due to anomalous fluid/rock interactions such as restricted diffusion, mixed-wettability and internal gradients. Deviations from the default properties have also been observed for certain crude oils, leading to inaccurate predictions in the model analysis. Through the use of D-T2 maps, the MEP approach provides a simple graphical representation of the data that can be used to identify fluid responses in all environments. Diffusion-Relaxation maps are further described in commonly assigned U.S. Pat. Nos. 6,570,382 and 6,462,542.
While these prior art methods are useful in predicting the presence of hydrocarbons in the formations, it is desirable to have simpler methods that can predict the presence of hydrocarbons in the formations from NMR data and are generally applicable to NMR data acquired with different pulse sequences. Furthermore, while two and three dimensional visualization has been developed to obtain primarily qualitative information, it is desirable to have quantitative interpretation techniques that can provide accurate fluid-characterization results.